Both the systems are initialized with a random
population / agents , and a search is made for the optimum by
updating generations .

However , in PSO we do not use evolutionary
operators like crossover , mutation etc . In this case , the
particles fly through the space by following current optimum
particles , Compared to Genetic Algorithms , PSO is easier to
implement and there are fewer parameters to adjust .

Both PSO and GA use some amount of randomization
in decision making . PSO is highly dependant on stochastic
processes like Evolutionary Computing . The adjustment towards
gbest and pbest is somewhat similar to the crossover operator .
It uses the idea of fitness . However , the unique idea is to
'fly' through the space in search for a better solution .